Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78596
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dc.contributor.authorYu, Sicheng
dc.date.accessioned2019-06-24T05:38:59Z
dc.date.available2019-06-24T05:38:59Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/78596
dc.description.abstractThe purpose of this study is to develop a method for scoring the motive imageries in text materials. According to Winter’s motive scoring system, there are three different kinds of motive imageries and each of them is given detailed definitions and scoring rules. But it’s difficult and also time-consuming to implement these rules manually. The traditional machine learning methods also have difficulties in extracting features. With the evolution and development of deep learning, deep neural networks have played an important role in data processing. In the study, three different deep learning models, including TextCNN, LSTM and Bidirectional LSTM with attention mechanism, are applied to score the motives. The performances of three models are evaluated, compared, and reported in this thesis.en_US
dc.format.extent69 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processingen_US
dc.titleMotive imagery scoring based on deep neural networken_US
dc.typeThesis
dc.contributor.supervisorChen Lihuien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US
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